Industrializing AI-powered drug discovery: lessons learned from the <i>Patrimony</i> computing platform
Mickaël Guedj, Jack Swindle, Antoine Hamon, Sandra Hubert, Emiko Desvaux, Jessica Laplume, Laura Xuereb, Céline Lefèbvre, Yannick Haudry, Christine Gabarroca, Audrey Aussy, Laurence Laigle, Isabelle Dupin‐Roger, Philippe Moingeon
Abstract
INTRODUCTION: was built up on the initial predicate to capitalize on our proprietary data while leveraging public data sources in order to foster a Computational Precision Medicine approach with the power of artificial intelligence. AREAS COVERED: is designed to identify novel therapeutic target candidates. With several successful use cases in immuno-inflammatory diseases, and current ongoing extension to applications to oncology and neurology, we document how this industrial computational platform has had a transformational impact on our R&D, making it more competitive, as well time and cost effective through a model-based educated selection of therapeutic targets and drug candidates. EXPERT OPINION: We report our achievements, but also our challenges in implementing data access and governance processes, building up hardware and user interfaces, and acculturing scientists to use predictive models to inform decisions.